{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n    @Author: King\\n    @Date: 2019.06.01\\n    @Purpose: DGL at a Glance\\n    @Introduction:   DGL is a Python package dedicated to deep learning on graphs, built atop existing tensor DL frameworks (e.g. Pytorch, MXNet) and simplifying the implementation of graph-based neural networks.\\n    @Datasets: \\n    @Link : \\n    @Reference : https://docs.dgl.ai/tutorials/basics/1_first.html\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "    @Author: King\n",
    "    @Date: 2019.06.01\n",
    "    @Purpose: DGL at a Glance\n",
    "    @Introduction:   DGL is a Python package dedicated to deep learning on graphs, built atop existing tensor DL frameworks (e.g. Pytorch, MXNet) and simplifying the implementation of graph-based neural networks.\n",
    "    @Datasets: \n",
    "    @Link : \n",
    "    @Reference : https://docs.dgl.ai/tutorials/basics/1_first.html\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 0: Problem description\n",
    "\n",
    "We start with the well-known “Zachary’s karate club” problem. The karate club is a social network which captures 34 members and document pairwise links between members who interact outside the club. The club later divides into two communities led by the instructor (node 0) and the club president (node 33). The network is visualized as follows with the color indicating the community:\n",
    "\n",
    "![karate-clu](img/karate-club.png)\n",
    "\n",
    "The task is to predict which side (0 or 33) each member tends to join given the social network itself.\n",
    "该任务是用于预测每个成员趋向于加入给定社交网络中的哪一方（0 or 33）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Creating a graph in DGL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 34 nodes.\n",
      "We have 156 edges.\n"
     ]
    }
   ],
   "source": [
    "# Creating the graph for Zachary’s karate club goes as follows:\n",
    "import dgl\n",
    "\n",
    "def build_karate_club_graph():\n",
    "    g = dgl.DGLGraph()\n",
    "    # add 34 nodes into the graph; nodes are labeled from 0~33\n",
    "    g.add_nodes(34)\n",
    "    # all 78 edges as a list of tuples\n",
    "    edge_list = [(1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2),\n",
    "        (4, 0), (5, 0), (6, 0), (6, 4), (6, 5), (7, 0), (7, 1),\n",
    "        (7, 2), (7, 3), (8, 0), (8, 2), (9, 2), (10, 0), (10, 4),\n",
    "        (10, 5), (11, 0), (12, 0), (12, 3), (13, 0), (13, 1), (13, 2),\n",
    "        (13, 3), (16, 5), (16, 6), (17, 0), (17, 1), (19, 0), (19, 1),\n",
    "        (21, 0), (21, 1), (25, 23), (25, 24), (27, 2), (27, 23),\n",
    "        (27, 24), (28, 2), (29, 23), (29, 26), (30, 1), (30, 8),\n",
    "        (31, 0), (31, 24), (31, 25), (31, 28), (32, 2), (32, 8),\n",
    "        (32, 14), (32, 15), (32, 18), (32, 20), (32, 22), (32, 23),\n",
    "        (32, 29), (32, 30), (32, 31), (33, 8), (33, 9), (33, 13),\n",
    "        (33, 14), (33, 15), (33, 18), (33, 19), (33, 20), (33, 22),\n",
    "        (33, 23), (33, 26), (33, 27), (33, 28), (33, 29), (33, 30),\n",
    "        (33, 31), (33, 32)]\n",
    "    # add edges two lists of nodes: src and dst\n",
    "    src, dst = tuple(zip(*edge_list))\n",
    "    g.add_edges(src, dst)\n",
    "    # edges are directional in DGL; make them bi-directional\n",
    "    g.add_edges(dst, src)\n",
    "\n",
    "    return g\n",
    "\n",
    "\n",
    "# We can print out the number of nodes and edges in our newly constructed graph:\n",
    "G = build_karate_club_graph()\n",
    "print('We have %d nodes.' % G.number_of_nodes())\n",
    "print('We have %d edges.' % G.number_of_edges())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt     #导入绘图包matplotlib（需要安装，方法见第一篇笔记）        \n",
    "# Since the actual graph is undirected, we convert it for visualization\n",
    "# purpose.\n",
    "nx_G = G.to_networkx().to_undirected()\n",
    "# Kamada-Kawaii layout usually looks pretty for arbitrary graphs\n",
    "pos = nx.kamada_kawai_layout(nx_G)\n",
    "nx.draw(nx_G, pos, with_labels=True, node_color=[[.7, .7, .7]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: assign features to nodes or edges\n",
    "\n",
    "Graph neural networks associate features with nodes and edges for training. For our classification example, we assign each node’s an input feature as a one-hot vector: node $v_i‘s$ feature vector is [0,…,1,…,0], where the $i_th$ position is one.\n",
    "\n",
    "In DGL, we can add features for all nodes at once, using a feature tensor that batches node features along the first dimension. This code below adds the one-hot feature for all nodes:\n",
    "我们可以一次为所有节点添加特性，使用沿第一个维度批处理节点特性的特性张量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "# 构建对角线上值为 1 的矩阵\n",
    "G.ndata['feat'] = torch.eye(34)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
      "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,\n",
      "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "# We can print out the node features to verify:\n",
    "# print out node 2's input feature\n",
    "print(G.nodes[2].data['feat'])\n",
    "\n",
    "# print out node 10 and 11's input features\n",
    "print(G.nodes[[10, 11]].data['feat'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: define a Graph Convolutional Network (GCN)\n",
    "\n",
    "![](img/step3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# Define the message & reduce function\n",
    "# NOTE: we ignore the GCN's normalization constant c_ij for this tutorial.\n",
    "def gcn_message(edges):\n",
    "    # The argument is a batch of edges.\n",
    "    # This computes a (batch of) message called 'msg' using the source node's feature 'h'.\n",
    "    return {'msg' : edges.src['h']}\n",
    "\n",
    "def gcn_reduce(nodes):\n",
    "    # The argument is a batch of nodes.\n",
    "    # This computes the new 'h' features by summing received 'msg' in each node's mailbox.\n",
    "    return {'h' : torch.sum(nodes.mailbox['msg'], dim=1)}\n",
    "\n",
    "# Define the GCNLayer module\n",
    "class GCNLayer(nn.Module):\n",
    "    def __init__(self, in_feats, out_feats):\n",
    "        super(GCNLayer, self).__init__()\n",
    "        self.linear = nn.Linear(in_feats, out_feats)\n",
    "\n",
    "    def forward(self, g, inputs):\n",
    "        # g is the graph and the inputs is the input node features\n",
    "        # first set the node features\n",
    "        g.ndata['h'] = inputs\n",
    "        # trigger message passing on all edges\n",
    "        g.send(g.edges(), gcn_message)\n",
    "        # trigger aggregation at all nodes\n",
    "        g.recv(g.nodes(), gcn_reduce)\n",
    "        # get the result node features\n",
    "        h = g.ndata.pop('h')\n",
    "        # perform linear transformation\n",
    "        return self.linear(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, the nodes send information computed via the message functions, and aggregates incoming information with the reduce functions.\n",
    "\n",
    "We then define a deeper GCN model that contains two GCN layers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a 2-layer GCN model\n",
    "class GCN(nn.Module):\n",
    "    def __init__(self, in_feats, hidden_size, num_classes):\n",
    "        super(GCN, self).__init__()\n",
    "        self.gcn1 = GCNLayer(in_feats, hidden_size)\n",
    "        self.gcn2 = GCNLayer(hidden_size, num_classes)\n",
    "\n",
    "    def forward(self, g, inputs):\n",
    "        h = self.gcn1(g, inputs)\n",
    "        h = torch.relu(h)\n",
    "        h = self.gcn2(g, h)\n",
    "        return h\n",
    "# The first layer transforms input features of size of 34 to a hidden size of 5.\n",
    "# The second layer transforms the hidden layer and produces output features of\n",
    "# size 2, corresponding to the two groups of the karate club.\n",
    "net = GCN(34, 5, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: data preparation and initialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use one-hot vectors to initialize the node features. Since this is a semi-supervised setting, only the instructor (node 0) and the club president (node 33) are assigned labels. The implementation is available as follow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = torch.eye(34)\n",
    "labeled_nodes = torch.tensor([0, 33])  # only the instructor and the president nodes are labeled\n",
    "labels = torch.tensor([0, 1])  # their labels are different"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5: train then visualize\n",
    "\n",
    "The training loop is exactly the same as other PyTorch models. We (1) create an optimizer, (2) feed the inputs to the model, (3) calculate the loss and (4) use autograd to optimize the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\progrom\\python\\python\\python3\\lib\\site-packages\\dgl\\base.py:18: UserWarning: Initializer is not set. Use zero initializer instead. To suppress this warning, use `set_initializer` to explicitly specify which initializer to use.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 | Loss: 0.6467\n",
      "Epoch 1 | Loss: 0.5209\n",
      "Epoch 2 | Loss: 0.4186\n",
      "Epoch 3 | Loss: 0.3317\n",
      "Epoch 4 | Loss: 0.2572\n",
      "Epoch 5 | Loss: 0.1891\n",
      "Epoch 6 | Loss: 0.1337\n",
      "Epoch 7 | Loss: 0.0910\n",
      "Epoch 8 | Loss: 0.0590\n",
      "Epoch 9 | Loss: 0.0371\n",
      "Epoch 10 | Loss: 0.0230\n",
      "Epoch 11 | Loss: 0.0142\n",
      "Epoch 12 | Loss: 0.0089\n",
      "Epoch 13 | Loss: 0.0056\n",
      "Epoch 14 | Loss: 0.0036\n",
      "Epoch 15 | Loss: 0.0023\n",
      "Epoch 16 | Loss: 0.0016\n",
      "Epoch 17 | Loss: 0.0011\n",
      "Epoch 18 | Loss: 0.0008\n",
      "Epoch 19 | Loss: 0.0006\n",
      "Epoch 20 | Loss: 0.0004\n",
      "Epoch 21 | Loss: 0.0003\n",
      "Epoch 22 | Loss: 0.0002\n",
      "Epoch 23 | Loss: 0.0002\n",
      "Epoch 24 | Loss: 0.0001\n",
      "Epoch 25 | Loss: 0.0001\n",
      "Epoch 26 | Loss: 0.0001\n",
      "Epoch 27 | Loss: 0.0001\n",
      "Epoch 28 | Loss: 0.0001\n",
      "Epoch 29 | Loss: 0.0001\n"
     ]
    }
   ],
   "source": [
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.01)\n",
    "all_logits = []\n",
    "for epoch in range(30):\n",
    "    logits = net(G, inputs)\n",
    "    # we save the logits for visualization later\n",
    "    all_logits.append(logits.detach())\n",
    "    logp = F.log_softmax(logits, 1)\n",
    "    # we only compute loss for labeled nodes\n",
    "    loss = F.nll_loss(logp[labeled_nodes], labels)\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    print('Epoch %d | Loss: %.4f' % (epoch, loss.item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a rather toy example, so it does not even have a validation or test set. Instead, Since the model produces an output feature of size 2 for each node, we can visualize by plotting the output feature in a 2D space. The following code animates the training process from initial guess (where the nodes are not classified correctly at all) to the end (where the nodes are linearly separable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.animation as animation\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def draw(i):\n",
    "    cls1color = '#00FFFF'\n",
    "    cls2color = '#FF00FF'\n",
    "    pos = {}\n",
    "    colors = []\n",
    "    for v in range(34):\n",
    "        pos[v] = all_logits[i][v].numpy()\n",
    "        cls = pos[v].argmax()\n",
    "        colors.append(cls1color if cls else cls2color)\n",
    "    ax.cla()\n",
    "    ax.axis('off')\n",
    "    ax.set_title('Epoch: %d' % i)\n",
    "    nx.draw_networkx(nx_G.to_undirected(), pos, node_color=colors,\n",
    "            with_labels=True, node_size=300, ax=ax)\n",
    "\n",
    "fig = plt.figure(dpi=150)\n",
    "fig.clf()\n",
    "ax = fig.subplots()\n",
    "draw(0)  # draw the prediction of the first epoch\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ani = animation.FuncAnimation(fig, draw, frames=len(all_logits), interval=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
